The present invention relates to the field of positron imaging, and more particularly to positron emission tomography detector efficiency normalization.
Positron emission tomography (PET) is a branch of nuclear medicine in which a positron-emitting radiopharmaceutical such as 18F-fluorodeoxyglucose (FDG) is introduced into the body of a patient. As the radiopharmaceutical decays, positrons are generated. More specifically, each of a plurality of positrons reacts with an electron in what is known as a positron annihilation event, thereby generating a coincident pair of 511 keV gamma rays which travel in opposite directions along a line of response (LOR).
A PET scanner is used to detect the positron annihilation events and generate an image of at least portions of the patient from a plurality of detected events. The PET scanner comprises a plurality of radiation-sensitive PET detectors arrayed about an examination region through which a patient is conveyed. The PET detectors typically comprise crystals and photomultiplier tubes (PMT's), wherein the detector crystals, referred to as scintillators, convert the energy of a gamma ray into a flash of light that is sensed by the detector PMT. In coincidence mode a gamma ray pair detected within a coincidence time by a pair of PET detectors is recorded by the PET scanner as an annihilation event; in an alternative singles mode technique a gamma ray detected by a single PET detector may be recorded as an annihilation event. During a patient scan hundreds of million of events are typically detected and recorded. The observed events are typically sorted and organized with respect to each of a plurality of projection rays, wherein all events occurring along each projection ray are organized into one bin of a three-dimensional sinogram array, the array typically stored in a computer-readable memory media. The sinogram data is then processed to reconstruct an image of the patient.
Prior to image reconstruction, efficiency normalization techniques are used to correct the sinogram data for non-uniform PET detector responses due to PET scanner geometry, detector crystal non-uniformity, and gain variation in detector PMT's. Efficiency normalization is generally accomplished through direct or component techniques. In direct efficiency normalization, sinogram data acquired from the scan of a special geometry phantom object (such as a cylinder, a rotating plane source, or a rotating line source) is inverted to calibrate a normalization factor directly, after removing known effects of source geometry, attenuation, random and scatter. However, statistical accuracy concerns proscribe direct efficiency normalization for large sinogram sizes.
Component efficiency normalization is generally preferred as better accommodating statistical noise and phantom geometry issues for large sinogram sizes. Component efficiency normalization is categorized by the decomposition of detector normalization into discrete factors (or components) wherein each factor may be calibrated separately. Component efficiency normalization is first approached as two-dimensional and modeled by two categories of factors: detector geometry factors and crystal efficiency factors. Detector geometry factors comprise circular detector geometry and solid angle, gamma ray incident angle and crystal depth of interaction. Crystal efficiency normalization is necessitated by the non-uniform response of detector crystals and their related PMTs. Crystal efficiency factors comprise intrinsic efficiency and deadtime effect. See M. Casey, H. Gadagkar and D. Newport in “A Component Based Method for Normalization in Volume PET”, 1995 International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine.
Two-dimensional component efficiency normalization may be extended to three dimensions (3D) and be made more complex by recognizing and calculating additional component factors. In one technique for block-detector PET scanners a detector block crystal interference pattern factor is added into the detector geometry term, and the normalization is extended from 2D to 3D; See P. Kinahan, D. Townsend, D. Bailey, D. Sashin, F. Jadali and M. Mintun in “Efficiency Normalization Technique for 3D PET Data”, IEEE NSS & MIC Record, Vol. 2, October 1995. In another technique a time-window alignment factor is added into the model; See R. Badawi, N. Ferreira, S Kohlmyer, M. Dahlbom, P. Marsden and T. Lewellen, “A comparison of normalization effects on three whole-body cylindrical 3D PET systems”, Phys. Med. Biol., Vol. 45, 2000.
However, adding additional factors to provide 3D detector efficiency normalization necessarily adds complexity to the normalization model. Moreover, some factors may not be applicable to the efficiency normalization of all PET scanners. For example, some non-block design PET scanners incorporate a pixilated detector module design wherein detector PMT edge rows are shared by neighboring detector modules and controlled by overlapping trigger channels: this type of design enables detector efficiency normalization independent of detector block crystal interference pattern factors.
What is needed is a method and system for 3D component efficiency normalization that provides for a reduced number of component factors and that will provide for efficient 3D PET scanner detector calibration.